Systems Theory Research Articles

Application of Time-Variant Systems Theory to the Unsteady Aerodynamics of Rotary Wings

This work employs the principles of time-variant systems theory to investigate the unsteady aerodynamics of rotary-wing configurations under periodic equilibrium conditions. Their application enables an extension of the pulse technique for system identification, as well as the adaptation of the linear-frequency-domain formulation commonly utilized in fixed-wing to rotary-wing scenarios. These methodologies effectively incorporate the aerodynamic nonlinearities associated with the equilibrium state into an efficient time-variant linearized representation of the unsteady aerodynamics. To promote its application in the context of rotary-wing aeroelasticity, a state-space realization based on a periodic autoregressive model with exogenous input is subsequently employed. Upon transformation from discrete to continuous time, the resulting aerodynamic model adopts a linear continuous-time periodic state-space formulation, offering compatibility for its coupling with a wide range of structural models. The proposed aerodynamic framework tailored to rotary-wing aeroelasticity holds applicability across a spectrum of aerodynamic models of arbitrary complexity, spanning from incompressible potential flow approximations to potentially more sophisticated methods. Showcasing the potential of this framework, the widely studied lossy Mathieu equation and the aerodynamic response to a flap perturbation about the periodic equilibrium condition of a prototypical rotor blade section, incorporating nonlinearities through an analytical dynamic stall model, are considered.

Stellarator equilibrium axis-expansion to all orders in distance from the axis for arbitrary plasma beta

A systematic theory of the asymptotic expansion of the magnetohydrostatics (MHS) equilibrium in the distance from the magnetic axis is developed to include arbitrary smooth currents near the magnetic axis. Compared with the vacuum and the force-free system, an additional magnetic differential equation must be solved to obtain the pressure-driven currents. It is shown that there exist variables in which the rest of the MHS system closely mimics the vacuum system. Thus, a unified treatment of MHS fields is possible. The mathematical structure of the near-axis expansions to arbitrary order is examined carefully to show that the double-periodicity of physical quantities in a toroidal domain can be satisfied order by order. The essential role played by the leading-order Birkhoff–Gustavson normal form in solving the magnetic differential equations is highlighted. Several explicit examples of vacuum, force-free and MHS equilibrium in different geometries are presented.

Machine Learning Models for Predictive Maintenance in Industrial Engineering

Purpose: The general objective of this study was to investigate machine learning models for predictive maintenance in industrial engineering. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.Findings: The findings reveal that there exists a contextual and methodological gap relating to machine learning models for predictive maintenance in industrial engineering. The research highlighted the transformative potential of machine learning models in optimizing predictive maintenance for industrial engineering, demonstrating significant reductions in unplanned downtime and maintenance costs. It identified the strengths of various machine learning approaches, such as supervised, unsupervised, and reinforcement learning, in predicting equipment failures and optimizing maintenance schedules. Despite the benefits, challenges such as data quality, integration complexity, and the need for specialized skills were noted. Future advancements in machine learning, IoT data, and computational power were expected to further enhance predictive maintenance systems, making them more accurate, efficient, and widely adopted across industries.Unique Contribution to Theory, Practice and Policy: The Systems Theory, Diffusion of Innovations Theory and Resource-Based View (RBV) Theory may be used to anchor future studies on machine learning models for predictive maintenance in industrial engineering. This study provided several recommendations that contributed to theory, practice, and policy. It emphasized the development of hybrid machine learning models, integration of domain-specific knowledge, and real-time data collection using IoT technologies. It suggested standardized data protocols and personnel training for better implementation and efficiency. Policy recommendations included regulatory frameworks, incentives for technology adoption, data sharing, and robust data privacy guidelines. These contributions aimed to enhance the accuracy and applicability of predictive maintenance models, improve industrial maintenance practices, and support technological innovation through supportive policies.

From quantum field theory to the quantum method the inverse problem On the 90th anniversary of the birth of academician L.D. Faddeev

The article is dedicated to the life and work of a theoretical physicist and mathematician, a full member of the USSR Academy of Sciences and RAS L.D. Faddeev (1934−2017), the formation of his scientific school. Faddeev’s works on scattering theory, quantum field theory, classical and quantum theory of integrable systems were included in the golden fund of world science and largely determined the face of modern mathematical physics. The author of the article is an employee of the Laboratory of Mathematical Problems of Physics created by L.D. Faddeev at the St. Petersburg Department of the V.A. Steklov Mathematical Institute of the Russian Academy of Sciences since its foundation.

Modeling and Laboratory Investigation of Tack Coats as Bituminous Pavement Interlayer

The adhesive properties of tack coats between asphalt pavement layers are crucial for the pavement’s structural behavior. This study first involved numerical analyses to compare stress patterns, deformations, and displacements in the pavement structure under various geometric and mechanical conditions. A rational calculation method based on the theory of elastic multilayer systems was used to quantify the impact of layer properties such as thickness, stiffness modulus, and Poisson’s ratio on interlayer bonding. Three bonding conditions—Full Friction, Partial Bonding, and Full Debonding—were analyzed to understand the tack coat’s effect between the top two layers. The second phase involved characterizing the mechanical behavior of the interface through shear strength tests (Leutner shear test) on both laboratory-prepared specimens and samples from a 10-year-old highway. Specimens were prepared using a Roller Compactor and tested under different interface conditions: hot-on-hot (H/H), residual bitumen 200 g/m2 (RB 200), and residual bitumen 400 g/m2 (RB 400). The tests examined the bonding effects in terms of tangential force and shear displacement at failure, as well as the impact of vehicular traffic on rutting and fatigue failure. Finally, this study investigated the long-term aging effects of the binder on interlayer bonding and sought to correlate the results of numerical calculations with those of the laboratory tests.

The Effectiveness of Enterprise Resource Planning (ERP) Systems in Knowledge Management

Purpose: The general objective of the study was to evaluate the effectiveness of Enterprise Resource Planning (ERP) systems in knowledge management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the effectiveness of Enterprise Resource Planning (ERP) systems in knowledge management. Preliminary empirical review revealed that ERP systems significantly enhanced knowledge management by integrating business processes, eliminating data silos, and improving decision-making and operational efficiency. Success depended on factors like organizational culture, top management support, and user training. ERP systems facilitated continuous improvement and innovation by providing real-time data access, enabling swift responses to market changes. Despite challenges like high costs and resistance to change, these could be mitigated through careful planning, strategic alignment, and effective change management, making the investment in ERP systems worthwhile for improved KM and organizational performance. Unique Contribution to Theory, Practice and Policy: The Resource-Based View (RBV) Theory, Socio-Technical Systems (STS) Theory and Knowledge-Based View (KBV) Theory may be used to anchor future studies on Enterprise Resource Planning (ERP) systems. The study recommended integrating ERP systems into KM frameworks to enhance theoretical models, emphasizing strategic alignment and continuous user engagement for practical implementation. It advised organizations to develop policies prioritizing ERP and KM alignment, foster knowledge-sharing cultures, and implement robust change management strategies. Continuous improvement and innovation were highlighted, with recommendations for regular system evaluation and incorporation of technological advancements. Long-term strategic planning was also emphasized, including comprehensive plans for ERP implementation, maintenance, and alignment with organizational goals to maximize the effectiveness of ERP systems in knowledge management.

Antecedents of sustainable agricultural supply chain financing for achieving food security in resource‐constrained developing economies: A complex adaptive systems theory perspective

Antecedents of sustainable agricultural supply chain financing for achieving food security in resource‐constrained developing economies: A complex adaptive systems theory perspective

From Me to You to Us: Exploring the Relationship Between Self-Care, Partner Communication, and Family Harmony

At its core, the family unit is composed of individual members. The way in which an individual engages in self-care practices has been shown to influence their ability to interact with others. Minimal research has explored the impact of an individual’s self-care on broader family functioning. Through a systems theory lens, this paper aims to elucidate two specific mechanisms through which self-care may affect family harmony through dyadic communication constructs. A parallel mediation model was tested using a sample of participants who completed the ELEVATE relationship education program ( N = 1578). Findings revealed two positive indirect effects of self-care on family harmony via affectionate communication and negative conflict management. Results also indicated a positive direct relationship between self-care and family harmony. This study represents an important empirical step towards understanding the complex relationships among intrapersonal self-care, interpersonal communication, and broader family harmony. Theoretical and practical implications are discussed.

Smart Grid Technologies and Their Role in Sustainable Energy Management

Purpose: The general objective of this study was to analyze smart grid technologies and their role in sustainable energy management. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to smart grid technologies and their role in sustainable energy management. Preliminary empirical review revealed that smart grid technologies significantly transformed energy management by enhancing sustainability, reliability, and efficiency through advanced metering, real-time analytics, and improved communication networks. These technologies enabled better integration of renewable energy sources, reduced greenhouse gas emissions, and empowered consumers with detailed energy usage information, leading to cost savings and increased energy efficiency. However, challenges such as high implementation costs, infrastructure needs, regulatory hurdles, and cybersecurity risks were identified. The study highlighted the importance of continued research, supportive policies, and public engagement to optimize smart grid deployment and achieve a more sustainable energy system. Unique Contribution to Theory, Practice and Policy: The Diffusions of Innovations Theory, Socio- Technical Systems Theory and Actor- Network Theory may be used to anchor future studies on smart grid technologies. The study recommended adopting a multidisciplinary approach to smart grid research, enhancing interoperability through standardized protocols, and investing in workforce training. It emphasized supportive regulatory frameworks, including incentives and dynamic pricing models, to promote smart grid adoption. Public awareness and engagement were deemed crucial, suggesting educational campaigns to increase consumer participation. The study highlighted the importance of international collaboration for knowledge sharing and called for continuous investment in research and development to drive technological advancements and ensure the sustainability and efficiency of smart grid systems.

Internet of Things (IoT) for Environmental Monitoring

Purpose: The general objective of this study was to explore the Internet of Things for environmental monitoring. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the Internet of Things for environmental monitoring. Preliminary empirical review revealed that IoT technologies have significantly enhanced environmental management practices by revolutionizing data collection and analysis across various ecosystems. By integrating IoT sensors with existing monitoring frameworks, real-time data on air and water quality, agriculture, wildlife habitats, and urban green spaces was efficiently gathered. This data facilitated proactive decision-making, early detection of environmental risks, and evidence-based policy formulation to address climate change, biodiversity conservation, and sustainable resource management challenges. Despite challenges like data security and interoperability, collaborative efforts among stakeholders paved the way for more effective environmental monitoring and sustainable development initiatives globally. Unique Contribution to Theory, Practice and Policy: The Complex Adaptive Systems Theory, Diffusion of Innovations Theory and Resource Dependence Theory may be used to anchor future studies on the Internet of Things technology. The study provided several recommendations that contributed significantly to theory, practice, and policy in environmental management. The study emphasized interdisciplinary approaches to enhance theoretical frameworks, advocating for advanced models and algorithms integrating IoT with environmental science and data analytics. In practice, it recommended widespread adoption of IoT-enabled sensor networks with enhanced capabilities for precise and reliable data collection. Policy-wise, the study called for regulatory frameworks supporting IoT integration, data standards, and international cooperation to address global environmental challenges collaboratively. Capacity building and continuous research and development were also highlighted to optimize IoT technologies for sustainable environmental monitoring and management globally.

Reasons for Barries in Family-School Cooperation

The concept of family-school cooperation was proposed very early, and its importance has always been emphasized. However, due to the excessive acceleration of the life cycle and the extension of working hours, families have unilaterally delegated the responsibility for children's education to schools. This negative situation for student growth needs to be reversed. Families must realize that the role of the school is to complement the work initiated by the family, not to replace it. Schools also need to realize that without the cooperation of families, school functions and outcomes will be limited. However, awareness is not enough, it is necessary to know the causes of this phenomenon. Therefore, this paper conducted a systematic review of previously published research on the causes of barriers to family-school cooperation. Based on Bronfenbrenner's ecological systems theory and Epstein's overlapping spheres of influence theory, the reasons for obstacles in family-school cooperation will be analyzed from different perspectives

The Influence of Family Relationships on Adolescent Generalized Anxiety Disorder

This comprehensive review synthesizes a wide array of studies investigating the intricate relationship between family dynamics and adolescent Generalized Anxiety Disorder (GAD) using a literature review approach. Briefly analyzed and summarized the relationship between Generalized Anxiety Disorder and theories such as social learning theory, psychodynamic theory, and Bronfenbrenners ecological systems theory. The consensus across numerous investigations suggests a substantial impact of family relationships on the prevalence and manifestation of GAD among adolescents. Notably, findings consistently underscore the significance of parent-adolescent interactions and the quality of inter-parental relationships in shaping adolescent anxiety levels. However, most studies employ cross-sectional designs, limiting the establishment of conclusive long-term causal links. Also, prevalent self-report evaluations for anxiety disorders among adolescents pose limitations, lacking comprehensive, objective assessments that may affect research outcomes. Addressing these limitations in future studies is critical to comprehensively elucidate the nuanced relationship between adolescent GAD and family dynamics.

Non-Hermitian linear-response theory for spin diffusion in quantum systems

Spin transport theory of non-Hermitian quantum systems, where the non-Hermiticity led to a series of exotic phenomena when the system experiences dissipation to an environment is proposed. The goal is a better understanding of the spin diffusion in the one-dimensional non-Hermitian XXZ model which allows the transport coefficients be computed analytically. Moreover, we analyzed the electric transport in the one-dimensional non-Hermitian Hubbard model which is a very important model of electrons strongly correlated, where we have investigated the effect of non-Hermitian parameters like the imaginary hopping on AC and DC conductivities of the system. We analyzed the large U limit of this model, where such non-Hermiticity contributes with a minus sign in the virtual exchange of quasiparticles and the behavior of the ground state energy and low-lying excitations is reversed.

Secure consensus for positive multi‐agent systems with bumpless transfer control under Denial‐of‐Service attacks

AbstractThis article investigates the secure consensus control problem of positive multi‐agent systems under multiple‐mode Denial‐of‐Service (DoS) attacks. A bumpless transfer (BT) control based secure mode strategy is proposed to improve the switching transient performance due to the system being actively switched to secure mode when attack is detected. Considering possible time delays for agents dealing with the issues of DoS attacks, an asynchronous switching model is established between system models and communication topology. With the stability theory of positive systems, sufficient and necessary conditions are established to guarantee the positivity of multi‐agent systems under the secure mode strategy. Because the switching law caused by external attacks is unknown in advance, the BT control is specifically implemented through the controller gain interpolation technique. By multiple Lyapunov functions (MLFs) approach, secure consensus is achieved with a trade‐off among controller performance, attack parameters, and switching delays. Numerical examples are presented to demonstrate the effectiveness of the obtained results.

Influence of Machine Learning on Predictive Maintenance in Manufacturing in Kenya

Purpose: The aim of the study was to evaluate the influence of machine learning on predictive maintenance in manufacturing in Kenya. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Machine learning has significantly enhanced predictive maintenance in Kenya's manufacturing sector. By analyzing large datasets from equipment sensors, ML algorithms can predict equipment failures before they occur, minimizing downtime and maintenance costs. This proactive approach improves operational efficiency and extends equipment lifespan, benefiting manufacturers by reducing unplanned disruptions and enhancing overall productivity. Unique Contribution to Theory, Practice and Policy: Diffusion of innovations theory, resource-based view (RBV) & sociotechnical systems theory may be used to anchor future studies on the influence of machine learning on predictive maintenance in manufacturing in Kenya. Manufacturing firms should invest in comprehensive training programs to equip their workforce with the necessary skills to manage and interpret ML-driven maintenance systems. Governments and industry regulators should create incentives for the adoption of ML technologies in manufacturing.

Effects of Parental Childhood Maltreatment on Children's School Adaptation: An Intergenerational Perspective Based on Envy.

Previous studies have examined the impact of maternal childhood maltreatment (CM) on children's school adaptation (SA), neglecting the role of fathers, and the joint influence of both parents. Based on family systems theory, this study explored the intergenerational impact of parental CM on children's SA and the mediating role of benign envy (BE) and malicious envy (ME). A total of 334 elementary school students' BE, ME, and SA statuses and their parents' CM, BE, and ME statuses were collected to construct the intergenerational transmission models of BE and ME for fathers, mothers, and parents, respectively. The results revealed that maltreated fathers or mothers individually exerted negative impacts on their children's SA, but when parents acted jointly, only fathers' CM intergenerational influence was significant. Mediation effects demonstrated that, individually, maltreated fathers indirectly affected children's SA through children's BE; maltreated mothers impacted children's SA through the "mothers' BE→children's BE" mediating chain; however, when taking combined parental action, only mothers' intergenerational transmission chain was significantly present. Identifying different intergenerational influence mechanisms of maltreated parents on offspring's school adaptation broadens our understanding of the diverse parenting roles of parents. That is, fathers foster their offspring's environmental adaptability through encouraging external exploration, while mothers enhance socialization by nurturing internal emotional development. Formulating strategies to address the emotional issues of maltreated parents, especially mothers, is crucial for mitigating the intergenerational consequences of maltreatment and enhancing the offspring's adaptability.

The two faces of Artificial Intelligence (AI): Analyzing how AI usage shapes employee behaviors in the hospitality industry

AI has become a critical technology driving societal advancement and impacting various sectors, including hospitality. Technology has social attributes, and employees play a major role in its application. Exploring the impact of AI usage on employee behaviors is essential. Drawing on the conservation of resources theory and the approach/avoidance systems theory, this study constructs a dual-path model to explore the effect of AI usage on employee behaviors. Data from 280 hotel employees in China were collected at three time points. Results show that AI usage enhance technological self-efficacy of employees, thereby promoting prosocial service behavior. Conversely, it could also induce workplace anxiety of employees, consequently leading to work withdrawal behavior. An approach orientation amplified the impact of AI usage on technological self-efficacy, while an avoidance orientation heightened the impact of AI usage on workplace anxiety. The study discussed the theoretical and practical implications and offers suggestions for future research.

A family of generalized Liénard differential systems with an explicit limit cycle

One of the important problems in the qualitative theory of real planar differential systems is limit cycles i.e., isolated periodic solutions in the set of all periodic this concept was defined at the end of the 19th century by Poincaré, and with the works of van der Pol and Liénard. The significance of these isolated solutions lies in their key role in understanding the dynamics of a certain differential system. Afterward, Hilbert specified a list of 23 problems for the advancement of mathematical science, and from then it started intensive research on these problems throughout the 20th century. Of the 23 problems, only the so-called 16th Hilbert’s problem and the Riemann conjecture remain open until now. The second part of the 16th Hilbert problem has two parts and asks for an upper bound on the number of possible limit cycles and their positions for a planar polynomial differential system of a given degree. The theory of the limit cycle operating as an indispensable mathematical tool has found broad and important applications in modern physics, chemical, biological, ,and other fields. Then the progress of these fields, in turn, promotes limit cycle research. In this paper, we prove the existence and uniqueness of the limit cycle for a family of generalized polynomial Liénard differential systems, the explicit expression of this limit cycle is given. An example illustrating our results is additionally presented.

A Comprehensive Review Based on the Game Theory with Energy Management and Trading

This paper reviews the use of game theory tools to study the operation and design of modern power grids. The contribution of this work is to summarize the literature to highlight the versatile solution capability of game theory by focusing on the interconnected objectives of energy trading and energy management. This review was conducted with a focus on various applications in energy systems, including general energy markets, micro grids (MGs), virtual power plants (VPP), electric vehicles (EVs), and smart homes, and explores how game theory can summarize the solutions for pricing, bidding, demand side management, and resource optimization. A key finding is the suitability of game theory for modeling decentralized energy systems where strategic incentives can lead to outcomes that benefit both individuals and society. It also discusses the limitations, challenges, and potential benefits of game theory in complex power systems. This study provides researchers and policy makers with a comprehensive overview of current research and insights into the potential of game theory to shape the future of energy systems.

A network control theory pipeline for studying the dynamics of the structural connectome.

Network control theory (NCT) is a simple and powerful tool for studying how network topology informs and constrains the dynamics of a system. Compared to other structure-function coupling approaches, the strength of NCT lies in its capacity to predict the patterns of external control signals that may alter the dynamics of a system in a desired way. An interesting development for NCT in the neuroscience field is its application to study behavior and mental health symptoms. To date, NCT has been validated to study different aspects of the human structural connectome. NCT outputs can be monitored throughout developmental stages to study the effects of connectome topology on neural dynamics and, separately, to test the coherence of empirical datasets with brain function and stimulation. Here, we provide a comprehensive pipeline for applying NCT to structural connectomes by following two procedures. The main procedure focuses on computing the control energy associated with the transitions between specific neural activity states. The second procedure focuses on computing average controllability, which indexes nodes' general capacity to control the dynamics of the system. We provide recommendations for comparing NCT outputs against null network models, and we further support this approach with a Python-based software package called 'network control theory for python'. The procedures in this protocol are appropriate for users with a background in network neuroscience and experience in dynamical systems theory.